Abstract
Interconnectivity options for injection molding machines, e.g., communication interfaces such as OPC-UA, allow machine and process variables to be recorded in high resolution. This data can be used to improve quality monitoring, which may contribute to cost reductions by minimizing production waste or increasing the use of recycled material. Currently, for example, only small amounts of production waste can be recycled back into the process because the component quality otherwise shows a high fluctuation due to changes in material properties. Automated quality control and adjustment of the process parameters can counteract these fluctuations and thus enable a higher proportion of recyclate to be used in production. In addition to the resulting savings, production costs can also be reduced by increasing product quality. This reduces the rate of production waste, for example, which contributes significantly to more economical and sustainable production. For these reasons, control of the quality properties of the manufactured components has been sought in injection molding for decades. However, the control of component properties requires their direct measurement within the production cycle, which is often not possible, very cost-intensive and/or cannot be carried out non-destructively. For this reason, it is common practice to control machine or process variables that correlate with component quality instead. However, the injection molding process is affected by numerous non-measurable disturbance variables which influence the transmission behavior of the machine, so that identical process parameters do not result in identical process variable curves and finally do not result in identical component quality. Thus, it is necessary to develop an assistance system based on a digital twin of the injection molding process, which supports the machine operator in setting the process parameters of the injection molding machine in such a way that a desired part quality results. As part of this study, a digital twin of a real injection molding process was developed on an Arburg injection molding machine (Allrounder 470S, ARBURG GmbH + Co KG, Lossburg, Germany). Essentially, the work involved the following steps: Setting up a quality measuring cell that records the relevant component qualities, developing a software module that records all relevant machine and process variables cycle-related as single values and trajectories, and modeling the digital twin that predicts the resulting component quality on the basis of the recorded variables. A laboratory scale and a digital measuring projector were used to determine the quality characteristics, so that the component weight and dimensional accuracies, e.g., diameter and width, were measured from the injection-molded tamper-evident closure after each cycle and assigned to the recorded machine and process variables of the corresponding cycle. The machine and process variables were retrieved via the OPC-UA interface of the injection molding machine. Process variable trajectories, such as cavity pressure, cavity temperature, injection pressure and injection speed curves, must be recorded in high resolution for reliable modeling due to the short duration of the injection process. All machine and process variables as well as the quality variables measured after the cycle are stored in a database file assigned to the cycle number. With the data retrieved from a design of experiment divided into training and test data, different static and dynamic model structures were tested according to their best fit rates (BFR). The different modelling approaches can be divided into three categories: 1)Setpoint model: The machine setpoints are mapped directly to the resulting part quality. A Polynomial Regression (PR) model and a Multilayer Perceptron (MLP) were employed. 2)Measurement-features model: The final part quality is predicted from the machine setpoints and from features extracted from process measurements based on expert knowledge, i.e., maximum cavity pressure and temperature, or temperature in the cavity at the beginning of the injection phase. As for the setpoint models a PR model and a MLP were employed. 3)Internal dynamics model: A modern type of Recurrent Neural Networks (RNN), a Gated Recurrent Unit (GRU) is used to predict batch-end product quality from process value trajectories. The internal state of the GRU is mapped to the output via a feedforward Neural Network with a nonlinear hidden layer and a linear output layer. Since the injection molding process is a time-varying process switching between different machine internal controllers, the model was also divided into the three major phases of the processing cycle (injection, packing, cooling). Since the third phase maps the internal state to the output, it is additionally equipped with an MLP. If the BFR of the individual models are compared, it can be seen that even the simple setpoint models can predict the component quality very well. The 10th degree PR model, for example, achieves a BFR of 90%. The fact that the models which predict the part quality only on the basis of the parameters set on the machine achieve very good results in this test series could be due, among other things, to the fact that all disturbance variables affecting the process were excluded or kept constant as far as possible during the test. For the models that take into account features calculated from the trajectories in addition to the setpoints, the MLP with ten neurons in the hidden layer achieved the highest BFR of 93%. Compared to these two static model approaches, the dynamic GRU achieves only marginally better BFR. On the one hand, it is astonishing that these models can predict the part quality so well based on the raw data without any prior knowledge from experts; on the other hand, the high computational effort for the formation of a digital twin, especially for short cycle times, cannot be justified. For the actual digital twin, static model approaches were therefore used whose computing times are significantly shorter. While the pre-trained twin receives the new machine and process data after each cycle in live operation of the injection molding machine and predicts the component quality from this, it then compares this prediction with the measured quality variables and re-trains itself based on the error. In this way, it learns to describe the process even better over time. Using backpropagation, the digital twin can also calculate the optimum machine settings for a desired target variable of the quality characteristics.
About the Presenter
Marco Klute studied mechanical engineering with specialization in plastics engineering at the University of Kassel/ Germany. His master thesis focused on two-component injection molding of polyamide and liquid silicone rubber (LSR) using different surface treatments. Since 2017 he has been working as a research fellow at the Institute for Material Engineering - Polymer Engineering at the University of Kassel, in the field of natural based composites as well as simulation and machine learning. He is currently working on two-component injection molding of bio-based polymers regarding material development and analytics of adhesion characteristics as well as the digitalization of injection molding processes using digital twins (supervisor Prof. Dr.-Ing. Heim).
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